{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:54:50Z","timestamp":1781020490408,"version":"3.54.1"},"reference-count":59,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:00:00Z","timestamp":1661904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Taif University Researchers Supporting Project","award":["TURSP-2020\/36"],"award-info":[{"award-number":["TURSP-2020\/36"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abnormal events or incidents regarding traffic on different roads and highways, such as traffic jams, traffic congestion, and vehicle accidents. Mostly in accidents, people are helpless and some die due to the unavailability of emergency treatment on long highways and those places that are far from cities. This research proposes a methodology for detecting accidents automatically through surveillance videos. A review of the literature suggests that convolutional neural networks (CNNs), which are a specialized deep learning approach pioneered to work with grid-like data, are effective in image and video analysis. This research uses CNNs to find anomalies (accidents) from videos captured by the VTSS and implement a rolling prediction algorithm to achieve high accuracy. In the training of the CNN model, a vehicle accident image dataset (VAID), composed of images with anomalies, was constructed and used. For testing the proposed methodology, the trained CNN model was checked on multiple videos, and the results were collected and analyzed. The results of this research show the successful detection of traffic accident events with an accuracy of 82% in the traffic surveillance system videos.<\/jats:p>","DOI":"10.3390\/s22176563","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T03:55:38Z","timestamp":1662004538000},"page":"6563","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":93,"title":["Anomaly Detection in Traffic Surveillance Videos Using Deep Learning"],"prefix":"10.3390","volume":"22","author":[{"given":"Sardar Waqar","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Information Technology, University of Sialkot, Sialkot 51040, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qasim","family":"Hafeez","sequence":"additional","affiliation":[{"name":"School of Physic Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6643-6558","authenticated-orcid":false,"given":"Muhammad Irfan","family":"Khalid","sequence":"additional","affiliation":[{"name":"Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, SA, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1585-2962","authenticated-orcid":false,"given":"Roobaea","family":"Alroobaea","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1523-1330","authenticated-orcid":false,"given":"Saddam","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5045-7485","authenticated-orcid":false,"given":"Jawaid","family":"Iqbal","sequence":"additional","affiliation":[{"name":"School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasem","family":"Almotiri","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. 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